The present invention relates to a plant process control system, and in particular to a process control system suitable for a large-scale plant such as a thermal power plant. A control system according to the present invention is a multi-purpose adaptive control system in which an objective function such as time required for startup or life consumption set by an operator in consideration of situation can be optimized by means of self-learning.
In a large-scale complicated process such as a thermal power plant process, a process variable such as turbine main steam temperature or main steam pressure has strong nonlinearity with respect to the manipulation rate of a manipulating parameter for controlling the process variable so that it may become a desired value. In conventional systems, therefore, the whole-system model of the process is incorporated in the control system and it is attempted to optimize the manipulation rate of the manipulating parameter by using the whole-system model of the process and operations research as described in JP-B-63-33164. That is to say, nonlinearity is represented by a model as table information or represented by physical expressions, and the optimum value of the manipulation rate of the manipulating parameter is derived by using nonlinear programming represented by the complex method.
In a conventional control system with a built-in model, the control performance of the actual plant is directly affected by the precision of the model, and hence a question arises as to how to study the significance of the model.